Obstructive sleep apnea (OSA) is characterized by recurrent upper airway obstructions during sleep. The most common animal model of OSA is based on subjecting rodents to intermittent hypoxic exposures and does not mimic important OSA features, such as recurrent hypercapnia and increased inspiratory efforts. To circumvent some of these issues, a novel murine model involving non-invasive application of recurrent airway obstructions was developed. An electronically controlled airbag system is placed in front of the mouse's snout, whereby inflating the airbag leads to obstructed breathing and spontaneous breathing occurs with the airbag deflated. The device was tested on 29 anesthetized mice by measuring inspiratory effort and arterial oxygen saturation (SaO(2)). Application of recurrent obstructive apneas (6s each, 120/h) for 6h resulted in SaO(2) oscillations to values reaching 84.4 +/- 2.5% nadir, with swings mimicking OSA patients. This novel system, capable of applying controlled recurrent airway obstructions in mice, is an easy-to-use tool for investigating pertinent aspects of OSA.

New techniques for automatic invasive and noninvasive identification of inspiratory flow limitation (IFL) are presented. Data were collected from 11 patients with full nocturnal polysomnography and gold-standard esophageal pressure (Pes) measurement. A total of 38,782 breaths were extracted and automatically analyzed. An exponential model is proposed to reproduce the relationship between Pes and airflow of an inspiration and achieve an objective assessment of changes in upper airway obstruction. The characterization performance of the model is appraised with three evaluation parameters: mean-squared error when estimating resistance at peak pressure, coefficient of determination, and assessment of IFL episodes. The model's results are compared to the two best-performing models in the literature. The obtained gold-standard IFL annotations were then employed to train, test, and validate a new noninvasive automatic IFL classification system. Discriminant analysis, support vector machines, and Adaboost algorithms were employed to objectively classify breaths noninvasively with features extracted from the time and frequency domains of the breaths' flowpatterns. The results indicated that the exponential model characterizes IFL and subtle relative changes in upper airway obstruction with the highest accuracy and objectivity. The new noninvasive automatic classification system also succeeded in identifying IFL episodes, achieving a sensitivity of 0.87 and a specificity of 0.85.